save_weights

FullyConnected.save_weights(filepath, overwrite=True, save_format=None, options=None)

Saves all layer weights.

Either saves in HDF5 or in TensorFlow format based on the save_format argument.

When saving in HDF5 format, the weight file has:

• layer_names (attribute), a list of strings

  (ordered names of model layers).

• For every layer, a group named layer.name

  • For every such layer group, a group attribute weight_names,

    a list of strings (ordered names of weights tensor of the layer).

  • For every weight in the layer, a dataset

    storing the weight value, named after the weight tensor.

When saving in TensorFlow format, all objects referenced by the network are saved in the same format as tf.train.Checkpoint, including any Layer instances or Optimizer instances assigned to object attributes. For networks constructed from inputs and outputs using tf.keras.Model(inputs, outputs), Layer instances used by the network are tracked/saved automatically. For user-defined classes which inherit from tf.keras.Model, Layer instances must be assigned to object attributes, typically in the constructor. See the documentation of tf.train.Checkpoint and tf.keras.Model for details.

While the formats are the same, do not mix save_weights and tf.train.Checkpoint. Checkpoints saved by Model.save_weights should be loaded using Model.load_weights. Checkpoints saved using tf.train.Checkpoint.save should be restored using the corresponding tf.train.Checkpoint.restore. Prefer tf.train.Checkpoint over save_weights for training checkpoints.

The TensorFlow format matches objects and variables by starting at a root object, self for save_weights, and greedily matching attribute names. For Model.save this is the Model, and for Checkpoint.save this is the Checkpoint even if the Checkpoint has a model attached. This means saving a tf.keras.Model using save_weights and loading into a tf.train.Checkpoint with a Model attached (or vice versa) will not match the Model’s variables. See the [guide to training checkpoints]( https://www.tensorflow.org/guide/checkpoint) for details on the TensorFlow format.

Parameters:

• filepath – String or PathLike, path to the file to save the weights to. When saving in TensorFlow format, this is the prefix used for checkpoint files (multiple files are generated). Note that the ‘.h5’ suffix causes weights to be saved in HDF5 format.

• overwrite – Whether to silently overwrite any existing file at the target location, or provide the user with a manual prompt.

• save_format – Either ‘tf’ or ‘h5’. A filepath ending in ‘.h5’ or ‘.keras’ will default to HDF5 if save_format is None. Otherwise, None becomes ‘tf’. Defaults to None.

• options – Optional tf.train.CheckpointOptions object that specifies options for saving weights.

Raises:

ImportError – If h5py is not available when attempting to save in HDF5 format.